Ranker ranker = new Ranker(); InfoGainAttributeEval ig = new InfoGainAttributeEval(); Instances instances = SamplesManager.asWekaInstances(trainSet); ig.buildEvaluator(instances); firstAttributes = ranker.search(ig,instances); candidates = Arrays.copyOfRange(firstAttributes, 0, FIRST_SIZE_REDUCTION); instances = reduceDimenstions(instances, candidates) PrincipalComponents pca = new PrincipalComponents(); pca.setVarianceCovered(var); ranker = new Ranker(); ranker.setNumToSelect(numFeatures); selection = new AttributeSelection(); selection.setEvaluator(pca); selection.setSearch(ranker); selection.SelectAttributes(instances ); instances = selection.reduceDimensionality(wekaInstances);
/** * Creates a default Ranker. * * @return the search algorithm */ public ASSearch getSearch() { return new Ranker(); }
/** Creates a default Ranker */ public ASSearch getSearch() { return new Ranker(); }
/** Creates a default Ranker */ public ASSearch getSearch() { return new Ranker(); }
/** Creates a default Ranker */ public ASSearch getSearch() { return new Ranker(); }
/** Creates a default Ranker */ public ASSearch getSearch() { return new Ranker(); }
/** Creates a default Ranker */ public ASSearch getSearch() { return new Ranker(); }
/** Creates a default Ranker */ public ASSearch getSearch() { return new Ranker(); }
/** Creates a default Ranker */ public ASSearch getSearch() { return new Ranker(); }
/** Creates a default Ranker */ public ASSearch getSearch() { return new Ranker(); }
/** Creates a default Ranker */ public ASSearch getSearch() { return new Ranker(); }
/** Creates a default Ranker */ public ASSearch getSearch() { return new Ranker(); }
/** Creates a default Ranker */ public ASSearch getSearch() { return new Ranker(); }
/** Creates a default Ranker */ public ASSearch getSearch() { return new Ranker(); }
/** Creates a default Ranker */ public ASSearch getSearch() { return new Ranker(); }
/** Creates a default Ranker */ public ASSearch getSearch() { return new Ranker(); }
/** Creates a default Ranker */ public ASSearch getSearch() { return new Ranker(); }
Normalize norm = new Normalize(); norm.setInputFormat(train); train = Filter.useFilter(train, norm); RemoveUseless ru = new RemoveUseless(); ru.setInputFormat(train); train = Filter.useFilter(train, ru); Ranker rank = new Ranker(); InfoGainAttributeEval eval = new InfoGainAttributeEval(); eval.buildEvaluator(train);
public void testPrincipalComponent() { m_Filter = getFilter(new weka.attributeSelection.PrincipalComponents(), new weka.attributeSelection.Ranker()); Instances result = useFilter(); assertTrue(m_Instances.numAttributes() != result.numAttributes()); }
public void testPrincipalComponent() { m_Filter = getFilter(new weka.attributeSelection.PrincipalComponents(), new weka.attributeSelection.Ranker()); Instances result = useFilter(); assertTrue(m_Instances.numAttributes() != result.numAttributes()); }